Use of Ant Colony Optimization (ACO) for Post-Deployment Replanning of UMTS Networks: Dual-Homing of RNCs for Handling Diurnal Mobility

Use of Ant Colony Optimization (ACO) for Post-Deployment Replanning of UMTS Networks: Dual-Homing of RNCs for Handling Diurnal Mobility

Samir K. Sadhukhan (IIM Calcutta, Kolkata, India), Chayanika Bose (Jadavpur University, Kolkata, India) and Debashis Saha (Indian Institute of Management (Calcutta), Kolkata, India)
Copyright: © 2018 |Pages: 30
DOI: 10.4018/IJAMC.2018040102
OnDemand PDF Download:
No Current Special Offers


Inherent dynamism in user movement demands for post-deployment tuning of UMTS networks to minimize the total cost of ownership (TCO). Conventionally, UMTS operators so far have considered many-to-one mapping of RNCs to MSCs. However, such single-homed networks do not remain cost-effective over the passage of time, typically when subscribers later on begin to show specific mobility patterns such as diurnal movement. This necessitates topological extension of the network in terms of dual-homing of some selected RNCs to two MSCs simultaneously via direct fibre links, resulting in a many-to-two mapping in parts of the network. The aim of such selective dual-homing is to reduce handoff cost maximally at the expense of minimal increase in link cost, thereby reducing the TCO optimally. In this article, the authors have formulated the scenario as an integer linear programming problem, converted it into a state space search and then solved it using Ant Colony Optimization (ACO) technique. Compared to Simulated Annealing (SA) and Tabu Search (TS), ACO exhibits 10% to 15% improvement in solution quality.
Article Preview

1. Introduction

With the prolific rise in the number of mobile terminals (MTs) having anytime access to any services anywhere, world-wide the operators of the universal mobile telecommunications system (UMTS) (Bott & Lescuyer, 2012) are forced to spend a large chunk of their expenditure not only on the spectrum license fee but also in the upgradation of their existing networks, which should be carefully planned to minimize both capital expenditure (CapEx) as well as operational expenditure (OpEx). CapEx includes the upfront investment made in enhancing the existing network infrastructure, while OpEx comprises recurring components such as handoff cost. The operators aim to minimize the total cost of ownership (TCO) which is the additive total of CapEx (amortized) and OpEx.

During the greenfield (pre-deployment) design of a network before the actual roll-out, a UMTS operator has only an estimate of the handoff cost, it will have to incur, based on an approximation of the handoff pattern that it anticipates a priori. Once the network starts functioning, this estimate often deviates from the actual scenario for several reasons including changes in the regional landscape, shift in user density, and gradual adoption of new services by the consumers. Also, the dynamic nature of the profiles of the subscribers gradually renders the operation of an existing network substantially different from the initial assumptions made by the greenfield designers. As time passes, all these effects slowly turn the original design of the network significantly sub-optimal in terms of TCO minimization. Hence, replanning of networks needs to be carried out periodically by the operators, subject to the existing deployment as constraint (in order to protect investment as much as possible). We term this replanning as the brownfield (post-deployment) tuning of an existing network, where one major concern of the operator is to fine-tune the network structure based on the exact handoff data collected over the past few years, in order to bring down the soaring handoff cost (OpEx). To do so, it may have to incur some CapEx (say adding a few extra links at some selected parts of the network), but the goal will again be to minimize the overall TCO. We consider this minimization problem here, but restrict ourselves to the context of post-deployment redesign only (Toril, Luna-Ramirez & Wille, 2013). Obviously, this redesign problem is quite different from the initial greenfield design problem (St-Hilaire & Liu, 2011) primarily due to the following two reasons: (a) the existing network acts as a constraint, and (b) the collected mobility data are to be taken into consideration.

It is now widely accepted by the network operators that, as far as post-deployment structural enhancement of a UMTS network is concerned (Toril, Luna-Ramirez & Wille, 2013; Razavi, Yuan, Gunnarsson & Moe, 2009), collected user mobility pattern (or a suitable model obtained therefrom) acts as an important tool for them to re-engineer their networks, particularly in respect of handoff cost minimization. In this context, most of the time it so happens that a clear pattern arises out of the evolving temporal mobility of subscribers (Razavi, Yuan, Gunnarsson & Moe, 2009; Sadhukhan, Mandal, Bhaumik & Saha, 2010b), and then we propose to go for dual-homing of radio network controllers (RNCs) (Sadhukhan et al., 2009) as a possible topological enhancement to reduce the handoff cost. Our idea is an extension of the similar works carried out for dual-homing in optical networks in order to provide reliable services (Nag, Payne & Ruffini, 2016; Izquierdo-Zaragoza et al., 2015; Abeywickramaa et al., 2016; Abdrabou, Hittini & Shaban, 2016). We have noted that, typically if a diurnal pattern (Sadhukhan, Mandal, Bhaumik & Saha, 2010b) appears to exist in the temporal mobility of subscribers, dual-homing of RNCs (where a few optimally selected RNCs are connected to two MSCs (as shown in Figure 1) is a useful strategy for post deployment fine-tuning of a UMTS network structure (Sadhukhan et al., 2009). Diurnal mobility refers to that special kind of mobility pattern, where an MT starts from a fixed source Node B and moves towards a fixed destination Node B every day, spends a long dwell time in the destination Node B, and then at the end of the day comes back to the source Node B (the journey is known as a diurnal tour) (Sadhukhan, Mandal, Bhaumik & Saha, 2010b).

Complete Article List

Search this Journal:
Volume 13: 4 Issues (2022): 2 Released, 2 Forthcoming
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing